High content time-lapse embryo imaging assessed by machine learning is revolutionizing the field of in vitro fertilization (IVF). However, the vast majority of IVF embryos are not transferred to the uterus, and these masses of embryos with unknown implantation outcomes are ignored in current efforts that aim to predict implantation. Here, we explore whether, and to what extent the information encoded within “sibling” embryos from the same IVF cohort contribute to the performance of machine learning-based implantation prediction. First, we show that the implantation outcome is correlated with attributes derived from the cohort siblings. Second, we demonstrate that this unlabeled data boosts implantation prediction performance. Third, we characterize the cohort properties driving embryo prediction, especially those that rescued erroneous predictions. Our results suggest that predictive models for embryo implantation can benefit from the overlooked, widely available unlabeled data of sibling embryos by reducing inherent noise of the individual transferred embryo.Significance statementWe use in vitro fertilization (IVF) as a model to study the effect of genotypic and environmental variation on phenotype and demonstrate a potential translational application. This is achieved by associating the implantation potential of transferred embryos and the visual information encoded within their non-transferred “sibling” embryos from the same IVF cohort, and establishing that these cohort features contribute to consistent improvement in machine learning implantation prediction regardless of the embryo-focused model. Our results suggest a general concept where the uncertainty in the implantation potential for the transferred embryo can be reduced by information encapsulated in the correlated cohort embryos. Since the siblings’ data are routinely collected, incorporating cohort features in AI-driven embryo implantation prediction can have direct translational implications.

High‐content time‐lapse embryo imaging assessed by machine learning is revolutionizing the field of in vitro fertilization (IVF). However, the vast majority of IVF embryos are not transferred to the uterus, and these masses of embryos with unknown implantation outcomes are ignored in current efforts that aim to predict implantation. Here, whether, and to what extent the information encoded within “sibling” embryos from the same IVF cohort contributes to the performance of machine learning‐based implantation prediction is explored. First, it is shown that the implantation outcome is correlated with attributes derived from the cohort siblings. Second, it is demonstrated that this unlabeled data boosts implantation prediction performance. Third, the cohort properties driving embryo prediction, especially those that rescued erroneous predictions, are characterized. The results suggest that predictive models for embryo implantation can benefit from the overlooked, widely available unlabeled data of sibling embryos by reducing the inherent noise of the individual transferred embryo.